Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human systemic lupus erythematosus patients to develop a transcriptional indicator of disease progression

ABSTRACT: Transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Experiment Overall Design: The proposed biomarker-selection strategy relies on modules for reducing highly dimensional microarray data sets in a stepwise manner. Starting from the full set of 28 modules, only those for which a set minimum proportion of transcripts are significantly changed between the study groups are selected (e.g., minimum proportion of differentially expressed transcripts at p < 0.05 = 15% overexpressed or underexpressed transcripts; in the example given, 11 SLE modules meet this criterion). This eliminates from the selection pool the modules registering fewer consistent changes that could be attributed to noise. Transcriptional vectors were derived for the entire cohort of 22 untreated pediatric SLE patients with the use of this set of 11 SLE modules. Patient profiles were also generated for an independent set of 31 children with SLE treated with steroids and/or cytotoxic drugs and/or hydroxychloroquine. A nonparametric method for analyzing multivariate ordinal data was used to score the patients. Lupus disease flares can lead to irreversible worsening of the patient's status. We tested the relevance of this multivariate transcriptional score for longitudinal monitoring of the disease activity in a cohort of 20 pediatric SLE patients (two to four time points/patient, intervals between each time point varied from one month to 18 months). Half of the patients had been included in our cross-sectional analysis before they were enrolled in this longitudinal study. Parallel trends were observed between multivariate transcriptional scores and a clinical severity score. The positive association was verified statistically with the use of a linear-regression model. Experiment Overall Design:

ORGANISM(S): Homo sapiens

SUBMITTER: Damien Chaussabel

PROVIDER: E-GEOD-11909 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.

Chaussabel Damien D Quinn Charles C Shen Jing J Patel Pinakeen P Glaser Casey C Baldwin Nicole N Stichweh Dorothee D Blankenship Derek D Li Lei L Munagala Indira I Bennett Lynda L Allantaz Florence F Mejias Asuncion A Ardura Monica M Kaizer Ellen E Monnet Laurence L Allman Windy W Randall Henry H Johnson Diane D Lanier Aimee A Punaro Marilynn M Wittkowski Knut M KM White Perrin P Fay Joseph J Klintmalm Goran G Ramilo Octavio O Palucka A Karolina AK Banchereau Jacques J Pascual Virginia V

Immunity 20080701 1

The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module l ...[more]

PMID: 18631455

Similar Datasets

Project description:The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings. Experiment Overall Design: Experiment subseries GSE11908 regroups the profiles that have been used to construct the modular transcriptional framework: Experiment Overall Design: The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the âclustering behaviorâ of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is âdata-drivenâ and does not involve manual selection of genes by the investigator. Experiment Overall Design: We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). Experiment Overall Design: The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules. Experiment Overall Design: Experiment subseries GSE11909 regroups the profiles that have been used to identify and validate biomarkers of SLE disease activity: Experiment Overall Design: The proposed biomarker-selection strategy relies on modules for reducing highly dimensional microarray data sets in a stepwise manner. Starting from the full set of 28 modules, only those for which a set minimum proportion of transcripts are significantly changed between the study groups are selected (e.g., minimum proportion of differentially expressed transcripts at p < 0.05 = 15% overexpressed or underexpressed transcripts; in the example given, 11 SLE modules meet this criterion). This eliminates from the selection pool the modules registering fewer consistent changes that could be attributed to noise. Transcriptional vectors were derived for the entire cohort of 22 untreated pediatric SLE patients with the use of this set of 11 SLE modules. Patient profiles were also generated for an independent set of 31 children with SLE treated with steroids and/or cytotoxic drugs and/or hydroxychloroquine. A nonparametric method for analyzing multivariate ordinal data was used to score the patients. Lupus disease flares can lead to irreversible worsening of the patient's status. We tested the relevance of this multivariate transcriptional score for longitudinal monitoring of the disease activity in a cohort of 20 pediatric SLE patients (two to four time points/patient, intervals between each time point varied from one month to 18 months). Half of the patients had been included in our cross-sectional analysis before they were enrolled in this longitudinal study. Parallel trends were observed between multivariate transcriptional scores and a clinical severity score. The positive association was verified statistically with the use of a linear-regression model.

Project description:The diagnosis of Systemic Lupus Erythematosus (SLE) is challenging due to its heterogeneous clinical presentation and lack of robust biomarkers for laboratory testing to distinguish it from other autoimmune or infectious diseases. Current diagnostic criterion relies on a combination of clinical examination and laboratory tests, and it does not readily distinguish between those with active clinical disease from those that are clinically quiescent. Several groups have attempted to apply emerging high throughput profiling technologies to diagnose SLE. Despite showing promising diagnostic potential, many of them are expensive and technically challenging for routine clinical uses. Here we report a pilot study of applying a technically simple and highly customisable leukocyte capture antibody microarray to profile healthy (n=24) and SLE patients (n=60) of various disease activities. Our analysis reveals a set of surface antigen biomarkers that have significant association with SLE. In addition, we present a computational method to calculate a score from the entire microarray profile to obtain a score that correlate most reliably with SLE disease activity. Although the current version of our microarray alone does not match the discriminatory power of the standard laboratory tests (serum dsDNA, complements C3 and C4), the combination of the two yields a test with significantly increased discriminatory ability than what can be achieved individually. This work paves the way for a customised SLE-specific antibody microarray for accurate diagnosis and monitoring of SLE that can be readily translated to routine clinical use. Blood samples were collected from 60 patients of Systemic Lupus Erythematosus (SLE) of different disease activity (11 active, 16 semi-active, and 33 inactive), and compared it with blood samples from 24 healthy blood donors. For each subject, we extracted PBMCs and apply them to a leukocyte capture antibody microarray. The microarray was then scaned by a DotScan™ slide reader (Medsaic; Eveleigh, NSW, Australia). The number of immobilized cells was proportional to the light scattered at each antibody spot. Binding of PBMCs to each antibody dot was monitored by light scatter and averaged between the duplicate spots on each slide. We then performed statistical analysis to identify CD antigen markers that are associated with SLE, as well as determining the potential diagnostic ability of this microarray for SLE.

Dataset Information

Transcription profiling of human systemic lupus erythematosus patients to develop a transcriptional indicator of disease progression

Publications

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure